Weekly Exercises #4

library(tidyverse)     # for data cleaning and plotting
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(carData)       # for Minneapolis police stops data
library(ggthemes)      # for more themes (including theme_map())
library(carData)
theme_set(theme_minimal())

# Starbucks locations
Starbucks <- read_csv("https://www.macalester.edu/~ajohns24/Data/Starbucks.csv")

starbucks_us_by_state <- Starbucks %>% 
  filter(Country == "US") %>% 
  count(`State/Province`) %>% 
  mutate(state_name = str_to_lower(abbr2state(`State/Province`))) 

# Lisa's favorite St. Paul places - example for you to create your own data
favorite_stp_by_lisa <- tibble(
  place = c("Home", "Macalester College", "Adams Spanish Immersion", 
            "Spirit Gymnastics", "Bama & Bapa", "Now Bikes",
            "Dance Spectrum", "Pizza Luce", "Brunson's"),
  long = c(-93.1405743, -93.1712321, -93.1451796, 
           -93.1650563, -93.1542883, -93.1696608, 
           -93.1393172, -93.1524256, -93.0753863),
  lat = c(44.950576, 44.9378965, 44.9237914,
          44.9654609, 44.9295072, 44.9436813, 
          44.9399922, 44.9468848, 44.9700727)
  )

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

Put your homework on GitHub!

If you were not able to get set up on GitHub last week, go here and get set up first. Then, do the following (if you get stuck on a step, don’t worry, I will help! You can always get started on the homework and we can figure out the GitHub piece later):

• Create a repository on GitHub, giving it a nice name so you know it is for the 4th weekly exercise assignment (follow the instructions in the document/video).
  
• Copy the repo name so you can clone it to your computer. In R Studio, go to file –> New project –> Version control –> Git and follow the instructions from the document/video.
  
• Download the code from this document and save it in the repository folder/project on your computer.
  
• In R Studio, you should then see the .Rmd file in the upper right corner in the Git tab (along with the .Rproj file and probably .gitignore).
  
• Check all the boxes of the files in the Git tab under Stage and choose commit.
  
• In the commit window, write a commit message, something like “Initial upload” would be appropriate, and commit the files.
  
• Either click the green up arrow in the commit window or close the commit window and click the green up arrow in the Git tab to push your changes to GitHub.
  
• Refresh your GitHub page (online) and make sure the new documents have been pushed out.
  
• Back in R Studio, knit the .Rmd file. When you do that, you should have two (as long as you didn’t make any changes to the .Rmd file, in which case you might have three) files show up in the Git tab - an .html file and an .md file. The .md file is something we haven’t seen before and is here because I included keep_md: TRUE in the YAML heading. The .md file is a markdown (NOT R Markdown) file that is an interim step to creating the html file. They are displayed fairly nicely in GitHub, so we want to keep it and look at it there. Click the boxes next to these two files, commit changes (remember to include a commit message), and push them (green up arrow).
  
• As you work through your homework, save and commit often, push changes occasionally (maybe after you feel finished with an exercise?), and go check to see what the .md file looks like on GitHub.
  
• If you have issues, let me know! This is new to many of you and may not be intuitive at first. But, I promise, you’ll get the hang of it!

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

These exercises will reiterate what you learned in the “Mapping data with R” tutorial. If you haven’t gone through the tutorial yet, you should do that first.

Starbucks locations (ggmap)

1. Add the Starbucks locations to a world map. Add an aesthetic to the world map that sets the color of the points according to the ownership type. What, if anything, can you deduce from this visualization?

Starbucks is not actually a franchise-based company. I believe that while many sales do come from the company-owned stores brewing and selling coffee, a good nearly (if not more) than half of total profits must be derived from already existing coffee companies paying (licensing) for the right to use the Starbucks’ name and likeness to sell caffeinated beverages.

world <- get_stamenmap(
    bbox = c(left = -180, bottom = -57, right = 179, top = 82.1), 
    maptype = "terrain",
    zoom = 2)

ggmap(world) +
  geom_point(data = Starbucks,
             aes( x = Longitude, y = Latitude, color = `Ownership Type`),
             alpha = 0.75,
             size = 1) + 
  labs(title = "Type of Starbuck Ownership Worldwide",
       x = "",
       y = "")

2. Construct a new map of Starbucks locations in the Twin Cities metro area (approximately the 5 county metro area).

mn_starbucks <- Starbucks %>% 
  filter(Country == "US", 
         `State/Province`== "MN")

tc_map <- get_stamenmap(
  bbox = c(left = -93.3540, bottom = 44.8217, right = -92.9723, top = 45.0672),
  maptype = "terrain",
  zoom = 11)

ggmap(tc_map) + 
  geom_point(data = mn_starbucks,
             aes(x = Longitude, 
                 y = Latitude),
             alpha = .5,
             size = 1.3) +
  theme_map() + 
  labs(title = "Starbuck Locations in the Twin Cities Metro Area")

3. In the Twin Cities plot, play with the zoom number. What does it do? (just describe what it does - don’t actually include more than one map).

Larger numbers on the zoom show more detail, as it means more pixels (or whatever.png is) are loaded into the map. Lower numbers include less details, so it creates a really awkward looking zoom with this smaller, concentrated area.

4. Try a couple different map types (see get_stamenmap() in help and look at maptype). Include a map with one of the other map types.

watery_coffee <- get_stamenmap(
  bbox = c(left = -93.3540, bottom = 44.8217, right = -92.9723, top = 45.0672),
  maptype = "watercolor",
  zoom = 11)

ggmap(watery_coffee) + 
  geom_point(data = mn_starbucks,
             aes(x = Longitude, 
                 y = Latitude),
             alpha = .5,
             size = 1.3) +
  theme_map() + 
  labs(title = "Starbuck Locations in the Twin Cities Metro Area",
       caption = "The basecolor looks like coffee stains... Starbucks?! What have you done this time?!")

5. Add a point to the map that indicates Macalester College and label it appropriately. There are many ways you can do think, but I think it’s easiest with the annotate() function (see ggplot2 cheatsheet).

ggmap(watery_coffee) + 
  geom_point(data = mn_starbucks,
             aes(x = Longitude, 
                 y = Latitude),
             alpha = .75,
             size = 1.3) +
  geom_point(aes(x = -93.1712321, y = 44.9378965)) +
  annotate("text", x = -93.1712321, y = 44.9356, label = "Macalester College") + 
  theme_map() + 
  labs(title = "Starbuck Locations in the Twin Cities Metro Area") +
  theme(legend.background = element_blank())

Choropleth maps with Starbucks data (geom_map())

The example I showed in the tutorial did not account for population of each state in the map. In the code below, a new variable is created, starbucks_per_10000, that gives the number of Starbucks per 10,000 people. It is in the starbucks_with_2018_pop_est dataset.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% #"census_pop_est_2018 is the name that the data read in after "<-" will be reachable at; read_csv and the following locates the data
  select(-dot) %>% #keeps all the data columns except "dot"
  mutate(state = str_to_lower(state)) #creates a new variable so that all the letters in the state variable are lowercase

starbucks_with_2018_pop_est <- 
  starbucks_us_by_state %>% #as to not delete the original dataset and creates a new name for further manipulations
  left_join(census_pop_est_2018,
            by = c("state_name" = "state")) %>% #this keeps all starbucks data and creates a new data set by matching the census data to the starbucks data
  mutate(starbucks_per_10000 = (n/est_pop_2018)*10000) #new variable created to create a per capita/ratio-type number of starbucks per population

6. dplyr review: Look through the code above and describe what each line of code does.

census_pop_est_2018 <- read_csv(“https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1”) %>% separate(state, into = c(“dot”,“state”), extra = “merge”) %>% #“census_pop_est_2018 is the name that the data read in after”<-” will be reachable at; read_csv and the following locates the data select(-dot) %>% #keeps all the data columns except “dot” mutate(state = str_to_lower(state)) #creates a new variable so that all the letters in the state variable are lowercase

starbucks_with_2018_pop_est <- starbucks_us_by_state %>% #as to not delete the original dataset and creates a new name for further manipulations left_join(census_pop_est_2018, by = c(“state_name” = “state”)) %>% #this keeps all starbucks data and creates a new data set by matching the census data to the starbucks data __mutate(starbucks_per_10000 = (n/est_pop_2018)*10000) #new variable created to create a per capita/ratio-type number of starbucks per population__

7. Create a choropleth map that shows the number of Starbucks per 10,000 people on a map of the US. Use a new fill color, add points for all Starbucks in the US (except Hawaii and Alaska), add an informative title for the plot, and include a caption that says who created the plot (you!). Make a conclusion about what you observe.

I see that Starbucks has the West Coast absolutely caffeine-dependent. All jokes aside, Washington, California, Oregon, Nevada, New Mexico, and Colorado are the highest Starbucks per capita states. I think Virginia is slightly lighter in the area due to Washington D.C.–politicans and NGO workers have to be functional. Same idea with Illinois in which Chicago and maybe Champaign-Urbana encourage the growth of Starbucks, but not enough to majorly impact the state’s representation overall. While NYC probably is a coffee hub for Starbucks, the amount of people probably far outweigh most, if not all, other states to be higher in the density of Starbucks.

states_map <- map_data("state")

starbucks_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = states_map, 
           aes(map_id = state_name,
               fill = starbucks_per_10000)) + 
  expand_limits(x = states_map$long, y = states_map$lat) +
  theme_map() +
  theme(legend.position = "none") +
  labs(title = "The Amount of Starbucks in the United States per 10000 People" ,
       subtitle = "Lighter shades of blue indicate a higher density of Starbucks",
       caption = "Created by Felicia Peterson, part-time graph-ter")

A few of your favorite things (leaflet)

8. In this exercise, you are going to create a single map of some of your favorite places! The end result will be one map that satisfies the criteria below.

• Create a data set using the tibble() function that has 10-15 rows of your favorite places. The columns will be the name of the location, the latitude, the longitude, and a column that indicates if it is in your top 3 favorite locations or not. For an example of how to use tibble(), look at the favorite_stp_by_lisa I created in the data R code chunk at the beginning.

felicia_faves <- tibble(
  place = c("Duplex", "Macalester College", "Estelle's", "Lake Harriet", "Costco", "Mississippi Market", "Dragon Star", "Trader Joe's", "Encore Boutique", "Shadow Falls Park"),
  long = c(-93.156860, -93.1712321,  -93.16385, 
           -93.304336, -93.13968, -93.165911, 
           -93.163495, -93.146174, -93.164599, -93.146789),
  lat = c(44.966510, 44.9378965, 44.934243,
          44.928458, 44.85781, 44.946946, 
          44.962957, 44.926591, 44.9665, 44.948591),
  top_3 = c(FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE)
)

• Create a leaflet map that uses circles to indicate your favorite places. Label them with the name of the place. Choose the base map you like best. Color your 3 favorite places differently than the ones that are not in your top 3 (HINT: colorFactor()). Add a legend that explains what the colors mean.

map_fp <- felicia_faves %>% 
  mutate(color = case_when(top_3 == TRUE ~ "red",
                           top_3 == FALSE ~ "blue"))

pal <- colorFactor("viridis", domain = felicia_faves$top_3)

leaflet(data = felicia_faves) %>% 
  addProviderTiles(providers$Stamen.Toner) %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             color = ~pal(top_3),
             label = ~place,
             weight = 11) %>% 
  addLegend(pal = pal,
            values = ~top_3,
            opacity = 0.5, 
            title = NULL, 
            position = "bottomright")

• Connect all your locations together with a line in a meaningful way (you may need to order them differently in the original data).

leaflet(data = map_fp) %>% 
  addProviderTiles(providers$Stamen.Toner) %>% 
  addCircles(
             color = map_fp$color,
             label = ~place) %>% 
  addPolylines(lng = ~long,
               lat = ~lat, 
               color = col2hex("darkred"))

• If there are other variables you want to add that could enhance your plot, do that now.

Revisiting old datasets

This section will revisit some datasets we have used previously and bring in a mapping component.

Bicycle-Use Patterns

The data come from Washington, DC and cover the last quarter of 2014.

Two data tables are available:

• Trips contains records of individual rentals
• Stations gives the locations of the bike rental stations

Here is the code to read in the data. We do this a little differently than usualy, which is why it is included here rather than at the top of this file. To avoid repeatedly re-reading the files, start the data import chunk with {r cache = TRUE} rather than the usual {r}. This code reads in the large dataset right away.

data_site <- 
  "https://www.macalester.edu/~dshuman1/data/112/2014-Q4-Trips-History-Data.rds" 
Trips <- readRDS(gzcon(url(data_site)))
Stations<-read_csv("http://www.macalester.edu/~dshuman1/data/112/DC-Stations.csv")

9. Use the latitude and longitude variables in Stations to make a visualization of the total number of departures from each station in the Trips data. Use either color or size to show the variation in number of departures. This time, plot the points on top of a map. Use any of the mapping tools you’d like.

w_dc <- get_stamenmap(
  bbox = c(left = -77.2394, bottom = 38.7589, right = -76.7896, top = 39.0288),
  maptype = "terrain",
  zoom = 10
)

ggmap(w_dc) 

throwback_bike <- Trips %>% 
  group_by(sstation, client) %>% 
  summarize(sum_station = n()) %>% 
  left_join(Stations,
            by = c("sstation" = "name"))

ggmap(w_dc) + 
  geom_point(data = throwback_bike,
             aes(x = long, y = lat, color = sum_station),
             alpha = .5, 
             size = .75) + 
  theme_map() + 
  labs(title = "Map of Washington DC's Bike Rental Stations", 
       subtitle = "Darker points indicate that more departures occurred from that location.") +
  theme(legend.background = element_blank(),
        legend.title = element_blank())

10. Only 14.4% of the trips in our data are carried out by casual users. Create a plot that shows which area(s) have stations with a much higher percentage of departures by casual users. What patterns do you notice? Also plot this on top of a map. I think it will be more clear what the patterns are.

The majority of casual users center around the most central part of Washington D.C., which makes sense because that area would be high density, as well as attract people who may be tourists/visiters.

casual_w_dc <- throwback_bike %>% 
  filter(client == "Casual") %>% 
  group_by(lat, long, sstation)

casual_w_dc

ggmap(w_dc) + 
  geom_point(data = casual_w_dc,
             aes(x = long, y = lat, size = casual_w_dc$sum_station),
             alpha = 0.5) + 
  theme_map() + 
  labs(title = "Map of Washington DC's Bike Rental Stations", 
       subtitle = "Larger circles indicate higher usage from clients registered as casual") +
  theme(legend.background = element_blank(),
        legend.title = element_blank())

COVID-19 data

The following exercises will use the COVID-19 data from the NYT.

11. Create a map that colors the states by the most recent cumulative number of COVID-19 cases (remember, these data report cumulative numbers so you don’t need to compute that). Describe what you see. What is the problem with this map?

It appears that California, Texas, Florida, and New York contain the most COVID-19 cases. The issue is that they also have higher populations in general. More accurate depictions of COVID-19 would be on a per capita of the population basis so we know the proportions within each state to accurately compare and contrast the cases.

states_map <- map_data("state")

covid19_states <- covid19 %>% 
  group_by(state) %>% 
  summarise(cases = last(cases)) %>% 
  mutate(state = str_to_lower(`state`))

covid19_states %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state,
               fill = cases)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "COVID-19 Cases in the United States") + 
  theme_map() + 
  theme(legend.background = element_blank())

12. Now add the population of each state to the dataset and color the states by most recent cumulative cases/10,000 people. See the code for doing this with the Starbucks data. You will need to make some modifications.

covid_19_state_populations <- covid19_states %>% 
  left_join(census_pop_est_2018,
            by = c("state" = "state"))

modified_covid19 <- covid_19_state_populations %>% 
  mutate(covid_prop = cases/est_pop_2018)  

modified_covid19 %>%
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state,
               fill = covid_prop)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  labs(title = "COVID-19 Cases in the United States",
       subtitle = "Lighter colors represent higher proportions of state population's COVID-19 cases") + 
  theme_map() + 
  theme(legend.background = element_blank(),
        legend.title = element_blank()) 

13. CHALLENGE Choose 4 dates spread over the time period of the data and create the same map as in exercise 12 for each of the dates. Display the four graphs together using faceting. What do you notice?

Minneapolis police stops

These exercises use the datasets MplsStops and MplsDemo from the carData library. Search for them in Help to find out more information.

14. Use the MplsStops dataset to find out how many stops there were for each neighborhood and the proportion of stops that were for a suspicious vehicle or person. Sort the results from most to least number of stops. Save this as a dataset called mpls_suspicious and display the table.

mpls_suspicious <- MplsStops %>% 
  group_by(neighborhood) %>% 
    mutate(is_it_suss = ifelse(problem == "suspicious", TRUE, FALSE)) %>% 
  summarise(stoppage = n(),
            suspicious_stop = sum(is_it_suss),
            prop_suspicious = suspicious_stop/stoppage) %>%
  arrange(desc(prop_suspicious))

mpls_suspicious

15. Use a leaflet map and the MplsStops dataset to display each of the stops on a map as a small point. Color the points differently depending on whether they were for suspicious vehicle/person or a traffic stop (the problem variable). HINTS: use addCircleMarkers, set stroke = FAlSE, use colorFactor() to create a palette.

sus_pal <- colorFactor("magma",
                       domain = MplsStops$problem)


leaflet(data = MplsStops) %>% 
  addTiles() %>% 
  addCircleMarkers(lng = ~long,
             lat = ~lat,
             stroke = FALSE,
             radius = 1,
            fillColor = ~sus_pal(problem))

16. Save the folder from moodle called Minneapolis_Neighborhoods into your project/repository folder for this assignment. Make sure the folder is called Minneapolis_Neighborhoods. Use the code below to read in the data and make sure to delete the eval=FALSE. Although it looks like it only links to the .sph file, you need the entire folder of files to create the mpls_nbhd data set. These data contain information about the geometries of the Minneapolis neighborhoods. Using the mpls_nbhd dataset as the base file, join the mpls_suspicious and MplsDemo datasets to it by neighborhood (careful, they are named different things in the different files). Call this new dataset mpls_all.

mpls_nbhd <- st_read("Minneapolis_Neighborhoods/Minneapolis_Neighborhoods.shp", quiet = TRUE)

mpls_all <- mpls_nbhd %>% 
  left_join(mpls_suspicious, 
            by = c("BDNAME" = "neighborhood")) %>% 
  left_join(MplsDemo,
            by = c("BDNAME" = "neighborhood"))

mpls_all

17. Use leaflet to create a map from the mpls_all data that colors the neighborhoods by prop_suspicious. Display the neighborhood name as you scroll over it. Describe what you observe in the map.

In general, people in neighborhoods in the southwest are stopped more for being deemed “suspicious” and overall, more neighborhoods in the south are stopped for “suspicious” behavior than their northern counterparts.

sus_prop_pal <- colorNumeric("Greens", 
                     domain = mpls_all$prop_suspicious) 

leaflet(data = mpls_all) %>% 
  addTiles() %>% 
  addPolygons(fillColor = ~sus_prop_pal(prop_suspicious),
             label = ~mpls_all$BDNAME,
             fillOpacity = .7)

18. Use leaflet to create a map of your own choosing. Come up with a question you want to try to answer and use the map to help answer that question. Describe what your map shows.

I wanted to compare this new map to the map made previously, in which we see that neighborhoods in the southwest are stopped more often, with the levels of poverty in Minneapolis.I could not figure out how to change the legend size, so I think that the darker the color, the higher the amount of poverty. There is a general north/south divide in poverty levels where the south is higher.

sus_bias <- colorFactor("plasma",
                        domain = mpls_all$poverty)

leaflet(data = mpls_all) %>% 
  addTiles() %>% 
  addPolygons(stroke = FALSE, 
              fillColor = ~sus_bias(poverty),
             label = ~mpls_all$BDNAME,
             fillOpacity = .7,
             highlight = highlightOptions(weight = 4,
                                          color = "black",
                                          fillOpacity = 0.75,
                                          bringToFront = FALSE)) %>% 
  addLegend(pal = sus_bias, 
            values = ~poverty, 
            bins = 4,
            opacity = 0.5, 
            title = "Poverty Levels",
            position = "bottomright")

GitHub link

19. Below, provide a link to your GitHub page with this set of Weekly Exercises. Specifically, if the name of the file is 04_exercises.Rmd, provide a link to the 04_exercises.md file, which is the one that will be most readable on GitHub.

The magical GitHub Link

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?